Automated System for Anatomical Vessel Characteristic Determination

ABSTRACT

A system enables a user to mark a ROI of a vessel portion having detected boundaries and extend the detection of vessel boundaries into a region identified by the user. An anatomical blood vessel detection and display system includes a user interface enabling a user, via a display of an image presenting anatomical vessels, to, mark a region of interest in a vessel identified by detected boundaries in the image and extend the marked region of interest of the vessel. A detector automatically detects a boundary of the vessel in the extended marked region of interest by detecting an edge representing a change in luminance in data representing the image. An image data processor identifies a minimum vessel diameter in the extended marked region of interest between the detected boundaries and initiates identification of the location of the minimum vessel diameter in the image.

FIELD OF THE INVENTION

This invention concerns an anatomical blood vessel detection and displaysystem enabling a user, via a display of an image presenting anatomicalvessels, to extend a marked region of interest of a vessel andautomatically detect a boundary of the vessel in the extended markedregion of interest.

BACKGROUND OF THE INVENTION

In known blood vessel detection applications, blood vessel detectioninvolves multiple user interactions making detection both time consumingand prone to error. Known systems for blood vessel detection experienceproblems including, erroneous detection of vessel boundaries, excessiveuse of computation resources, inflexibility. Known systems also oftenrequire burdensome iterative trial and error processing. A user of aknown system may inadvertently select a region outside of a vessel arearesulting in erroneous detection of a non-existent vessel boundary asillustrated in FIG. 1 in the erroneously detected boundary 103 in aregion of interest outside of a real vessel boundary. Known systems alsolack flexibility in enabling a user to extend an already detectedportion of a vessel boundary to a different vessel portion and mayrequire complete image vessel re-detection. Known methods typically donot allow extension of a detected vessel to an undetected portion. Knownmethods also do not allow a user to extend vessel boundary detection toa side branch of an already detected main vessel branch through abifurcation, for example.

Known vessel detection methods involve repetitive trial and error stepswhen smaller diameter vessels have to be detected and a detected vesselboundary may not fit an actual boundary. This results in a user havingto correct a detected boundary or delete the boundary in order tomanually mark the vessel in the region of interest and initiate vesseldetection to match the actual boundary. Further, known methods involveburdensome computation and perform vessel detection each time a userselects a region in the same image. This leads to unnecessary repetitivecomputation to detect a vessel boundary in the selected region ofinterest. Known methods also involve multiple manual user interactionsto edit a detected vessel to make the vessel detection match with anactual vessel boundary to move detection through a vessel bifurcation,for example. A system according to invention principles addresses thesedeficiencies and related problems.

SUMMARY OF THE INVENTION

A system enables a user to mark a ROI of a vessel portion havingdetected boundaries and extend the detection of vessel boundaries into aregion identified by the user and inhibit erroneous detection ofboundaries in an image area devoid of vessels. An anatomical bloodvessel detection and display system includes a user interface enabling auser, via a display of an image presenting anatomical vessels, to, marka region of interest in a vessel identified by detected boundaries inthe image and extend the marked region of interest of the vessel. Adetector automatically detects a boundary of the vessel in the extendedmarked region of interest by detecting an edge representing a change inluminance in data representing the image. An image data processoridentifies a minimum vessel diameter in the extended marked region ofinterest between the detected boundaries and initiates identification ofthe location of the minimum vessel diameter in the image.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 shows a medical image indicating an erroneously detected vesselboundary in a region of interest outside of an actual vessel boundary.

FIG. 2 shows an anatomical blood vessel detection and display system,according to invention principles.

FIG. 3 shows a medical image indicating a portion of a detected vesselboundary and measurement of a minimal luminal diameter (MLD), accordingto invention principles.

FIG. 4 shows extending detected boundaries of a marked vessel inresponse to selecting a center point along the marked vessel, accordingto invention principles.

FIG. 5 shows extending detected boundaries of a marked vessel to a sidebranch, i.e. extending detection from non-bifurcation to bifurcation,according to invention principles.

FIG. 6 shows a flowchart of a process used by an anatomical blood vesseldetection and display system for extending detection of a vesselboundary and measurement of a minimal luminal diameter (MLD), accordingto invention principles.

FIG. 7 shows an input vessel image processed by a vessel boundarydetection system, according to invention principles.

FIG. 8 shows an image provided in response to edge detection performedin the input image of FIG. 7 by a vessel boundary detector, according toinvention principles.

FIG. 9 shows a histogram presenting pixel count versus luminanceintensity in an image of limited edge contrast prior to contraststretching, according to invention principles.

FIG. 10 shows a histogram presenting pixel count versus luminanceintensity employed by a vessel boundary detection system followingcontrast stretching, according to invention principles.

FIG. 11 shows an image provided in response to processing the inputimage of FIG. 7 by a vessel boundary detection system using contraststretching, according to invention principles.

FIG. 12 shows an outline of edges of vessels in the image of FIG. 11determined by a vessel boundary detector, according to inventionprinciples.

FIG. 13 shows the vessel image of FIG. 12 in response to curve fittingused to determine a vessel boundary outline, according to inventionprinciples.

FIG. 14A shows start and end points of a vessel in a ROI and FIG. 14Billustrates determination of a vessel boundary seed point in a ROI by avessel boundary detection system, according to invention principles.

FIGS. 15A and 15B illustrate determining a perpendicular line betweenvessel boundary points, according to invention principles.

FIG. 16 illustrates calculating a center point of a perpendicular linebetween vessel boundary points, according to invention principles.

FIG. 17 illustrates finding a next boundary point along a vesseldirection, according to invention principles.

FIG. 18 illustrates a center line and diameter of a vessel in a ROI,according to invention principles.

FIG. 19 shows a flowchart of a process employed by an anatomical bloodvessel detection and display system, according to invention principles.

DETAILED DESCRIPTION OF THE INVENTION

A system enables a user to mark a ROI of a vessel portion havingdetected boundaries and extend the automatic detection of vesselboundaries into a region identified by the user. The systemautomatically determines a minimum vessel diameter in the extendedmarked region of interest between the detected boundaries and initiatesidentification of the location of the minimum vessel diameter in theimage.

FIG. 2 shows anatomical blood vessel detection and display system 10.System 10 includes one or more processing devices (e.g., workstations orportable devices such as notebooks, computers, Personal DigitalAssistants, phones) 12 that individually include a user interfacecontrol device 31 such as a keyboard, mouse, touchscreen, voice dataentry and interpretation device and memory 28. System 10 also includesat least one repository 17, X-ray imaging modality system 25 (which inan alternative embodiment may comprise an MR (magnetic resonance), CTscan, or Ultra-sound system, for example) and server 20intercommunicating via network 21. X-ray modality system 25 comprises aC-arm X-ray radiation source and detector device rotating about apatient table and an associated electrical generator for providingelectrical power for the X-ray radiation system. The display images aregenerated in response to predetermined user (e.g., physician) specificpreferences. At least one repository 17 stores medical image studies formultiple patients in DICOM compatible (or other) data format. A medicalimage study individually includes multiple image series of a patientanatomical portion which in turn individually include multiple images.Server 20 includes, display processor 15, image data processor 29,vessel boundary detector 36 and system and imaging controller 34.Display processor 15 provides data representing display imagescomprising a Graphical User Interface (GUI) for presentation on display19 of processing device 12. Imaging controller 34 controls operation ofimaging device 25 in response to user commands entered via userinterface 31. In alternative arrangements, one or more of the units inserver 20 may be located in device 12 or in another device connected tonetwork 21.

Imaging system 25 acquires data representing images of vessels ofpatient anatomy. Display processor 15 enables a user, via a display ofan image on unit 19 presenting anatomical vessels, to, mark a region ofinterest in a vessel identified by detected boundaries in the image andto extend the marked region of interest of the vessel. Vessel boundarydetector 36 automatically detects a boundary of the vessel in theextended marked region of interest by detecting an edge representing achange in luminance in data representing the image. Image data processor29 identifies a minimum vessel diameter in the extended marked region ofinterest between the detected boundaries and initiates identification ofthe location of the minimum vessel diameter in the image.

System 10 automatically detects vessel boundaries within a whole imageusing vessel boundary detector 36. A user employs user interface device31 FIG. 2 to mark a region of interest (ROI), mark a particular vesselportion and to extend the vessel by automatically detecting a boundaryof the vessel in the extended marked region of interest. In extending avessel, a user marks two points in between highlighted detected vesselboundaries in the detected vessel in the marked ROI of the image. A usermarks a ROI on a detected vessel, rather than selecting a ROI fromwithin the image. This advantageously prevents a user from marking a ROIoutside a vessel boundary resulting in mismatch of marked and actualvessel and erroneous vessel boundary detection. System 10 enables a userto extend the marked vessel by selecting a center point along with aboundary of the marked ROI vessel. System 10 provides flexibility inextending already detected vessel boundaries, reducing computation timein marking a detected vessel in a ROI and ensuring a marked vesselmatches an actual vessel. The system minimises manual user interactioninvolved in performing deletion of a marked vessel and redrawing avessel boundary in response to erroneous boundary detection, forexample.

A user marks a vessel in a marked ROI and initiates extending the markedvessel boundaries by selecting a region of interest to mark a segment ofa vessel in an image that highlights vessels provided by vessel boundarydetector 36 in an initial detection operation. A user selects and markstwo center points representing start and end point in between a vesselboundary in a marked ROI as illustrated in FIG. 14A. In response tomarking the center points, vessel boundary detector 36 finds the nearestboundary points surrounding the start point and tracks and extends theboundary pixels having pixel location and direction determined bydetector 36.

FIG. 3 shows a medical image indicating a portion of a detected vesselboundary and measurement of a minimal luminal diameter (MLD). Detector36 tracks and extends the boundary pixels to provide the highlightedvessel portion 303 between the two white crosses in a ROI. Image dataprocessor 29 automatically identifies and measures minimal luminaldiameter (MLD) 305. The tracking of boundary pixels in a ROI ends whentracking reaches near the end point marked by the user. Whenever no pathis determined by detector 36 between start and end point marked by auser, detector 36 prompts a user via an image on display 19 to remarkthe ROI. In response to user marking of a vessel boundary in a ROI, auser is advantageously able to extend a marked vessel provided thatthere is a path or continuity in between the marked vessel and markedcenter point. Detector 36 employs the tracking process extendingboundary pixels in the direction of the vessel until the newly markedcenter point is reached. FIG. 4 shows extension of detected boundaries401 of a marked vessel by detector 36 in response to selection of centerpoint 403 along the marked vessel. FIG. 5 shows detector 36 extendingdetected boundaries of a marked vessel to a side branch 407, i.e.extending detection from non-bifurcation to bifurcation.

FIG. 6 shows a flowchart of a process used by vessel boundary detector36 for extending detection of a vessel boundary and measurement of aminimal luminal diameter (MLD). In step 606 following the start at step603, detector 36 performs edge extraction in a source image byidentifying blood vessels as edges comprising discontinuity of imagebrightness. Detector 36 generates a two dimensional convolution matrixfor extracting an edge pixel and performs a two dimensional convolutionwith the matrix to detects sharp variation of intensity around a pixel.In one embodiment a convolution filter of size 5×5 is applied to asource image to extract the features representing blood vessels in theimage. With a filter size 5×5, each pixel intensity is calculated bytaking the weighted average of the neighboring pixel intensities, i.e.pixels from two rows from top, bottom and left and right. A convolutionfilter matrix for edge extraction comprises,

−0.125000 −0.125000 −0.125000 −0.125000 1.000000 −0.125000 1.000000−0.125000 −0.125000 −0.125000 −0.125000 −0.125000 −0.125000 0.0625000.125000 0.125000 −0.125000 0.062500 0.125000 0.062500 1.000000−0.125000 0.125000 0.062500 0.125000.In each row or column of the filter matrix, the filter values arepositive or negative. So for pixels which are edge pixels, the weightedaverage of each pixel tends towards maxima and for the pixels withuniform intensities the weighted average tends towards zero. Thefiltered pixel luminance values are given by,

${{H\lbrack x\rbrack}\lbrack y\rbrack} = {\sum\limits_{j = 0}^{{height} - 1}{\sum\limits_{i = 0}^{{width} - 1}{{{F\left\lbrack {x + i} \right\rbrack}\left\lbrack {y + j} \right\rbrack}{{G\lbrack i\rbrack}\lbrack j\rbrack}}}}$

Height and width are the height and width of source image.G[i][j] is the pixel intensity value at row I and column jF[x+i][y+j] is the filter matrix entry at row x+I, and column y+j

FIG. 8 shows an image provided in response to edge detection performedin an input image shown in FIG. 7 by vessel boundary detector 36.

In step 609, detector 36 enhances edge contrast information withcontrast stretching for images (or a portion of an image) with reducedvariation of intensity of edges. Detector 36 determines upper and lowerpixel value limits over which the image is to be normalized andgenerates a histogram pixel count for each pixel intensity in thedetermined intensity range. In one embodiment, the determined intensityrange is specified as 0-255, for example. The intensity range on whichcontrast stretching is to be performed is specified as intensitiescovering the 14th and 86th percentile of total pixel count, for example.

FIG. 9 shows a histogram presenting pixel count versus luminanceintensity in an image of limited edge contrast prior to contraststretching. Detector 36 stretches luminance intensity values of thepixel luminance values of the FIG. 9 image using the function,

Pstretch=(Pin−C)(b−a/d−c)+a

where,

a and b are the lower and upper limit of allowed intensity, i.e. 0 to255

c and d are the intensities at 14th and 86th percentile of pixel count

Pin is input pixel intensity for stretching

Pstretch is stretched output pixel intensity.

FIG. 10 shows a histogram presenting pixel count versus luminanceintensity employed by a vessel boundary detection system followingcontrast stretching. FIG. 11 shows an image provided in response toprocessing the input image of FIG. 7 by vessel boundary detector 36using contrast stretching to perform contrast stretch on edge pixels.

In step 612, detector 36 detects edge pixels and edge pixel direction inthe contrast stretched pixel data. Detector 36 detects edge pixelmaxima, i.e. pixel luminance gradient transitions above a predeterminedthreshold value in horizontal and vertical directions for each pixel.Detector 36 also performs convolution of the horizontal and verticalmatrix for neighbouring pixels to approximate and filter gradient valuesof a pixel. The gradient at a point (xi,yi) is calculated byGradient(xi,yi)=sqrt(Gx²+Gy²), Gx and Gy are the gradient approximationsin X and Y direction.

In one embodiment, the convolution matrices for horizontal and verticalapproximation are,

$\begin{matrix}1 & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{matrix}$

Convolution matrix for horizontal approximation and

$\begin{matrix}1 & 2 & 1 \\0 & 0 & 0 \\{- 1} & {- 2} & {- 1}\end{matrix}$

Convolution matrix for vertical gradient approximation.

The gradient approximation values of each pixel in the horizontal andvertical direction are used to determine the edge direction at eachpixel location. Detector 36 uses an atan2 function of horizontal andvertical gradients to determine an edge direction at a pixel location.The atan2 function is a variation of the arctangent function and realarguments x and y are not both equal to zero, atan2(y, x) is the anglein radians between the positive x-axis of a plane and the point given bythe coordinates (x, y) on it. A measured angulation is approximated toangle values 0°, 45°, 90°, 135° with a downward angle direction withrespect to the horizontal axis (x-axis). Table 1 shows the approximationof the pixel direction to 0°, 45°, 90°, 135°.

Ø(i, j)=atan2(Gx,Gy), Gx and Gy are Gradient approximation in X and Ydirection.

TABLE 1 Approximation of the pixel direction Angle Range Adjusted angleDirection to next pixel −22.5 < ø < 22.5,  0° Direction to right 157.5 <ø < −157.5 horizontally 22.5 < ø < 67.5, 45° Direction 45° to x axis in−157.5 < ø < −112.5 downward direction 67.5 < ø < 112.5, 90° Directionperpendicular to −112.5 < ø < −67.5 x axis in downward direction 112.5 <ø < 157.5, 135°  Direction 135° to x axis in −67.5 < ø < −22.5 downwarddirection

In step 615, detector 36 traces edges in the image so that blood vesselsin a two dimensional image view appear as two substantially parallelcurves which are continuous but change orientation and vessel width.Detector 36 determines an outline of vessel edges with maxima gradientin the image and gradient and pixel direction of each pixel calculatedin step 612. Detector 36 traces the edges by including neighbouring edgepixels having substantially the same gradient direction in individualedge segments and by collating edge segments to form a vessel outline ofa detected edge in an image. FIG. 12 shows an outline of edges ofvessels in the image of FIG. 11 determined by detector 36 having pixelluminance values exceeding a predetermined threshold.

In step 617, detector 36 determines a vessel boundary by performing acurve fit for the detected edge pixel segments and detecting a vesselboundary for each segment of an edge. The vessel boundary pixels areestablished in one embodiment using a fifth order polynomial curve fit,for example. Detector 36 fits the polynomial to the X and Y co-ordinatevalues of sequential edge pixels and uses a minimum spanning tree foreach edge segment. The fifth order polynomial is,

F(x)=Σ(Cj*Xj); 0<=j<=5; F(x)=Y

i.e. Y=Σ(Cj*X ^(j))  equation (1).

Further, A and Y are matrices of X and Y co-ordinates of the edge pixelsrespectively. Equation (1) gives AC=Y where matrix C represents acoefficient or a least square residual determined from the input X and Yco-ordinates of equation (1). A transpose of both sides of equationAC=Y, provides equation (2).

$\begin{matrix}{{{{BC} = D};{B = {A*{A(t)}}}},{{D = {{A(t)}Y}};}} & {{equation}\mspace{14mu} (2)} \\{{A(t)}\mspace{14mu} {is}\mspace{14mu} {transpose}\mspace{14mu} {of}\mspace{14mu} {matrix}\mspace{14mu} A} & \; \\{{where}\mspace{14mu} {Matrix}\mspace{14mu} B\mspace{14mu} {is}} & \; \\\begin{matrix}N & {\Sigma ({Xi})} & {\Sigma \left( {Xi}^{2} \right)} & {\Sigma \left( {Xi}^{3} \right)} & {\Sigma \left( {Xi}^{4} \right)} & {\Sigma \left( {Xi}^{5} \right)} \\{\Sigma ({Xi})} & {\Sigma \left( {Xi}^{2} \right)} & {\Sigma \left( {Xi}^{3} \right)} & {\Sigma \left( {Xi}^{4} \right)} & {\Sigma \left( {Xi}^{5} \right)} & {\Sigma \left( {Xi}^{6} \right)} \\{\Sigma \left( {Xi}^{2} \right)} & {\Sigma \left( {Xi}^{3} \right)} & {\Sigma \left( {Xi}^{4} \right)} & {\Sigma \left( {Xi}^{5} \right)} & {\Sigma \left( {Xi}^{6} \right)} & {\Sigma \left( {Xi}^{7} \right)} \\{\Sigma \left( {Xi}^{3} \right)} & {\Sigma \left( {Xi}^{4} \right)} & {\Sigma \left( {Xi}^{5} \right)} & {\Sigma \left( {Xi}^{6} \right)} & {\Sigma \left( {Xi}^{7} \right)} & {\Sigma \left( {Xi}^{8} \right)} \\{\Sigma \left( {Xi}^{4} \right)} & {\Sigma \left( {Xi}^{5} \right)} & {\Sigma \left( {Xi}^{6} \right)} & {\Sigma \left( {Xi}^{7} \right)} & {\Sigma \left( {Xi}^{8} \right)} & {\Sigma \left( {Xi}^{9} \right)} \\{\Sigma \left( {Xi}^{5} \right)} & {\Sigma \left( {Xi}^{6} \right)} & {\Sigma \left( {Xi}^{7} \right)} & {\Sigma \left( {Xi}^{8} \right)} & {\Sigma \left( {Xi}^{9} \right)} & {\Sigma \left( {Xi}^{10} \right)}\end{matrix} & \; \\{{N = {{Number}\mspace{14mu} {of}\mspace{14mu} {input}\mspace{20mu} {points}\mspace{14mu} {for}\mspace{14mu} {curve}\mspace{14mu} {fit}}},} & \; \\{1<=i<={N\mspace{14mu} {and}\mspace{14mu} {Matrix}\mspace{14mu} D\mspace{14mu} {is}}} & \; \\{\Sigma ({yi})} & \; \\{\Sigma ({xiyi})} & \; \\{\Sigma \left( {{xi}^{2}{yi}} \right)} & \; \\{\Sigma \left( {{xi}^{3}{yi}} \right)} & \; \\{\Sigma \left( {{xi}^{4}{yi}} \right)} & \; \\{{\Sigma \left( {{xi}^{5}{yi}} \right)}.} & \;\end{matrix}$

The coefficient matrix C of equation (1) is obtained by Gaussianelimination with partial pivoting on B|D. A partial pivoting approach istaken in order to avoid a pivot element equal to zero. Substituting Cjand Xi in equation (1), gives the new Yi, 0<=j<=5, Xi is X co-ordinateat point i. The vessel boundary outline is determined with the curve fitY co-ordinate for each of the edge pixels. FIG. 13 shows the vesselimage of FIG. 12 in response to curve fitting by detector 36 used todetermine a vessel boundary outline.

Curve fit pixel values derived in step 617 for the detected edge pixelsegments are accumulated in step 620. Steps 617 and 620 are repeated fordifferent segments until it is determined in step 623 that a vesselboundary is detected for the multiple segments of edges of the wholeimage.

In step 626, detector 36 in response to user command entered via userinterface device 31, marks a ROI, marks a vessel, extends the markedvessel and measures a minimal luminal diameter (MLD). Detector 36 tracksa vessel boundary in a ROI and measures the MLD during marking a vesselor extending a marked vessel in a ROI. Detector 36 tracks a vesselboundary in a ROI, in response to the previously performed vesselboundary detection for the whole image and user marking of start and endpoint in a ROI as previously described in connection with FIG. 14A.

FIG. 14B illustrates determination of a vessel boundary seed point in aROI by detector 36. Detector 36 initially selects S1 as a seed point andlooks for a nearest vessel boundary pixel e.g. following a spiraloutward path until pixel V1 is detected. On reaching the boundary pixeldetected in step B, a pixel status of V1 is set as marked vessel. Inresponse to determining a starting boundary pixel in one boundary i.e.V1, a pixel at the corresponding opposite boundary of the vessel i.e. VPis marked by tracking in the direction perpendicular to boundary at V1.FIGS. 15A and 15B illustrate determining a perpendicular line betweenvessel boundary points V1 and V1′. The seed boundary point V1 may be ineither of the two boundary edges as illustrated in FIGS. 15A and 15Brespectively. Detector 36 traverses in a direction perpendicular to thepixel V1 on the vessel boundary to find angle Øv1 of perpendicular line469 with respect to the X axis for edge pixels. The direction of theperpendicular line can be upward (decreasing Y axis or row value),downward (increasing Y axis or row value), leftward (decreasing X axisor column value) rightward (increasing X axis or column value). Table 2shows the calculation of the angle of the perpendicular line withrespect to X axis.

TABLE 2 Calculation of the angle of the perpendicular line with respectto X axis V1V1′ direction Øv1 <V1V1′ to x axis Downward  0° (90° + Øv1)= 90° 45° (90° + Øv1) = 135° 135°  (90° + Øv1) = 45° Upward  0° (Øv1 −90°) = −90° 45° (Øv1 − 90°) = −45° 135°  (Øv1 − 90°) = −135° Leftward90° (90° + Øv1) = 180° Rightward 90° (Øv1 − 90°) = 0°

The path in between the pixels V1 and V1′ is perpendicular to the vesselboundary and the length of the path is the diameter of vessel at V1 andV1′. Detector 36 marks the seed boundary pixel in the ROI and calculatesthe diameter of the vessel at V1 and V1′ and calculates the center pointC1 of the diameter at V1V1′. FIG. 16 illustrates calculating centerpoint C1 of the perpendicular line between vessel boundary points V1 andV1′.

Upon marking the seed boundary pixel in the ROI and calculation of thediameter and center point of the vessel at the seed point, detector 36traces along the vessel boundary in the direction of last markedboundary pixel and marks the next boundary pixel. FIG. 17 illustratesfinding a next boundary point along a vessel direction. Detector 36calculates diameter and center point at each location during traversinga vessel boundary. Tracing of the boundary pixels ends on reaching neara user marked end point (e.g., point S2 of FIG. 14A). FIG. 18illustrates a center line and diameter at each pixel location of avessel in a ROI. Table 3 shows results of tracing of a vessel boundaryin the ROI.

TABLE 3 Boundary pixel and corresponding diameter Boundary PixelDiameter path Diameter Length MLD V1V1′ V1 . . . V1′ L1 . . . . . . . .. Lm VnVn′ Vn . . . Vn′ Ln

Upon the boundary pixels in the ROI being marked and vessel diameterbeing calculated along the vessel in the ROI, detector 36 determines theminimal luminal diameter by finding the minimum value of the diameterlength column of Table 3. In step 629, the process of FIG. 6 terminates.

System 10 (FIG. 2) accurately detects a vessel boundary and enables auser to mark a ROI on the detected vessel advantageously preventing auser from marking a ROI outside the vessel boundary and resultingmismatch of an incorrectly detected vessel and an actual vessel. System10 enables a user to extend an already marked vessel boundary in a ROIby marking a new center point facilitating switching vessel detectionfrom a non-bifurcation to a bifurcation in a vessel. The system improvesdetection for vessels with smaller diameter and uses a polynomial curvefit method for smoothing vessel edges (boundaries) and segmenting curvefit to groups of seven pixels, for example. System 10 further enablesreducing the number of pixels used in each curve fit iteration to reducesmooth error and improve vessel boundary detection. The system improvesdetection in a low contrast image by performing contrast stretch to aidin detection of edge pixels in an image having lower intensity variationbetween an edge pixel and its neighbouring pixel. In addition system 10reduces computation time by performing vessel boundary detection of anentire image and storing boundary pixel location and direction for usein subsequent vessel boundary detection in a region of interest selectedby a user. The storing and reuse of this pixel informationadvantageously reduces computation time during marking and extending avessel.

FIG. 19 shows a flowchart of a process employed by anatomical bloodvessel detection and display system 10 (FIG. 2). In step 912 followingthe start at step 911, a user interface enables a user, via a display ofan image generated by display processor 15 presenting anatomical vesselson display 19, to, mark a region of interest of a vessel identified bydetected boundaries in the image and extend the marked region ofinterest of the vessel by extending the marking of the detectedboundaries in the direction of a point selected by a user on the vesselinto a bifurcation, for example, via the user interface. The markedregion of interest of the vessel is extended by, identifying a firstpixel on one of the detected boundaries closest to a point selected by auser using the user interface, identifying a second pixel on the otherof the detected boundaries closest to the point selected by the user andextending marking of the detected boundaries beyond the marked region ofinterest by determining vessel boundary pixels in the image adjacent tothe first and second pixels. Further, the marked region of interest ofthe vessel is identified by visual attribute comprising at least one of,(a) highlighting, (b) different color, (c) different shading, (d)different pattern and (e) dashed line.

The user interface in step 915 prevents a user from marking the regionof interest outside a vessel boundary based on detected location of thevessel boundary. In step 917 vessel boundary detector 36 automaticallydetects a boundary of the vessel in the extended marked region ofinterest by detecting an edge representing a change in luminance in datarepresenting the image. Detector 36 detects the edge representing thechange in luminance in response to determining a luminance gradientexceeding a predetermined threshold, Detector 36 detects individualpixels comprising the edge representing the change in luminance inresponse to determining a luminance gradient exceeding a predeterminedthreshold and by fitting a curve to individual segmented groups ofsubstantially adjacent pixels. A segmented group of substantiallyadjacent pixels comprises a predetermined number of pixels. Further,detector 36 automatically identifies vessel boundaries having aluminance variation below a predetermined threshold and enhances theluminance variation using a contrast stretching function employing anenhancement factor derived using a ratio of a maximum luminanceintensity range to a predetermined portion of the maximum luminanceintensity range. The contrast stretching function comprises,Pstretch=(Pin−C)(b−a/d−c)+a, for example, as previously described.

In step 923, image data processor 29 identifies a minimum vesseldiameter in the extended marked region of interest between the detectedboundaries and in step 926 identifies the location of the minimum vesseldiameter in the image. The process of FIG. 19 terminates at step 933.

A processor as used herein is a device for executing machine-readableinstructions stored on a computer readable medium, for performing tasksand may comprise any one or combination of, hardware and firmware. Aprocessor may also comprise memory storing machine-readable instructionsexecutable for performing tasks. A processor acts upon information bymanipulating, analyzing, modifying, converting or transmittinginformation for use by an executable procedure or an information device,and/or by routing the information to an output device. A processor mayuse or comprise the capabilities of a controller or microprocessor, forexample, and is conditioned using executable instructions to performspecial purpose functions not performed by a general purpose computer. Aprocessor may be coupled (electrically and/or as comprising executablecomponents) with any other processor enabling interaction and/orcommunication there-between. A user interface processor or generator isa known element comprising electronic circuitry or software or acombination of both for generating display images or portions thereof. Auser interface comprises one or more display images enabling userinteraction with a processor or other device.

An executable application, as used herein, comprises code or machinereadable instructions for conditioning the processor to implementpredetermined functions, such as those of an operating system, a contextdata acquisition system or other information processing system, forexample, in response to user command or input. An executable procedureis a segment of code or machine readable instruction, sub-routine, orother distinct section of code or portion of an executable applicationfor performing one or more particular processes. These processes mayinclude receiving input data and/or parameters, performing operations onreceived input data and/or performing functions in response to receivedinput parameters, and providing resulting output data and/or parameters.A user interface (UI), as used herein, comprises one or more displayimages, generated by a user interface processor and enabling userinteraction with a processor or other device and associated dataacquisition and processing functions.

The UI also includes an executable procedure or executable application.The executable procedure or executable application conditions the userinterface processor to generate signals representing the UI displayimages. These signals are supplied to a display device which displaysthe image for viewing by the user. The executable procedure orexecutable application further receives signals from user input devices,such as a keyboard, mouse, light pen, touch screen or any other meansallowing a user to provide data to a processor. The processor, undercontrol of an executable procedure or executable application,manipulates the UI display images in response to signals received fromthe input devices. In this way, the user interacts with the displayimage using the input devices, enabling user interaction with theprocessor or other device. The functions and process steps herein may beperformed automatically or wholly or partially in response to usercommand. An activity (including a step) performed automatically isperformed in response to executable instruction or device operationwithout user direct initiation of the activity.

The system and processes of FIGS. 2-19 are not exclusive. Other systems,processes and menus may be derived in accordance with the principles ofthe invention to accomplish the same objectives. Although this inventionhas been described with reference to particular embodiments, it is to beunderstood that the embodiments and variations shown and describedherein are for illustration purposes only. Modifications to the currentdesign may be implemented by those skilled in the art, without departingfrom the scope of the invention. The system enables a user to mark a ROIof a vessel portion having detected boundaries and extend the automaticdetection of vessel boundaries into a region identified by the user.Further, the processes and applications may, in alternative embodiments,be located on one or more (e.g., distributed) processing devices on anetwork linking the units of FIG. 2. Any of the functions and stepsprovided in FIGS. 2-19 may be implemented in hardware, software or acombination of both.

What is claimed is:
 1. An anatomical blood vessel detection and displaysystem, comprising: a user interface enabling a user, via a display ofan image presenting anatomical vessels, to, mark a region of interest ofa vessel identified by detected boundaries in said image and extend themarked region of interest of said vessel; a detector for automaticallydetecting a boundary of said vessel in the extended marked region ofinterest by detecting an edge representing a change in luminance in datarepresenting said image; and an image data processor for identifying aminimum vessel diameter in the extended marked region of interestbetween the detected boundaries and for initiating identification of thelocation of the minimum vessel diameter in said image.
 2. A systemaccording to claim 1, wherein the marked region of interest of saidvessel is extended by extending the marking of said detected boundariessubstantially in the direction of a point selected by a user on saidvessel via said user interface.
 3. A system according to claim 2,wherein the marked region of interest of said vessel is identified byvisual attribute comprising at least one of, (a) highlighting, (b)different color, (c) different shading, (d) different pattern and (e)dashed line.
 4. A system according to claim 2, wherein the marked regionof interest of said vessel is extended by, identifying a first pixel onone of said detected boundaries closest to a point selected by a userusing said user interface, identifying a second pixel on the other ofsaid detected boundaries closest to said point selected by said user andextending marking of said detected boundaries beyond the marked regionof interest by determining vessel boundary pixels in said image adjacentto the first and second pixels.
 5. A system according to claim 1,wherein said detector automatically identifies vessel boundaries havinga luminance variation below a predetermined threshold and enhances saidluminance variation by use of an enhancement factor and detects a vesselboundary by detecting an edge representing a change in the enhancedluminance variation.
 6. A system according to claim 1, wherein saiddetector detects said edge representing said change in luminance inresponse to determining a luminance gradient exceeding a predeterminedthreshold.
 7. A system according to claim 1, wherein said detectorautomatically identifies vessel boundaries having a luminance variationbelow a predetermined threshold and enhances said luminance variationusing a contrast stretching function employing an enhancement factorderived using a ratio of a maximum luminance intensity range to apredetermined portion of the maximum luminance intensity range.
 8. Asystem according to claim 7, wherein said contrast stretching functioncomprises,Pstretch=(Pin−C)(b−a/d−c)+a where, a and b are the lower and upper limitof allowed intensity, c and d are the intensities at selectedpercentiles of pixel count, Pin input pixel intensity for stretching andPstretch is stretched output pixel intensity.
 9. A system according toclaim 1, wherein said detector detects individual pixels comprising saidedge representing said change in luminance in response to determining aluminance gradient exceeding a predetermined threshold and by fitting acurve to individual segmented groups of substantially adjacent pixels.10. A system according to claim 9, wherein a segmented group ofsubstantially adjacent pixels comprises a predetermined number ofpixels.
 11. A system according to claim 1, wherein said user interfaceenables a user to extend the marked region of interest of said vesselinto a bifurcation of said vessel.
 12. A method for anatomical bloodvessel detection and display system, comprising the activities of:enabling a user, via a display of an image presenting anatomicalvessels, to, mark a region of interest of a vessel identified bydetected boundaries in said image and extend the marked region ofinterest of said vessel; automatically detecting a boundary of saidvessel in the extended marked region of interest by detecting an edgerepresenting a change in luminance in data representing said image; andidentifying a minimum vessel diameter in the extended marked region ofinterest between the detected boundaries and for initiatingidentification of the location of the minimum vessel diameter in saidimage.
 13. A method according to claim 12, wherein the marked region ofinterest of said vessel is extended by extending the marking of saiddetected boundaries substantially in the direction of a point selectedby a user on said vessel.
 14. A method according to claim 13, whereinthe marked region of interest of said vessel is identified by visualattribute comprising at least one of, (a) highlighting, (b) differentcolor, (c) different shading, (d) different pattern and (e) dashed line.15. A method according to claim 13, wherein the marked region ofinterest of said vessel is extended by, identifying a first pixel on oneof said detected boundaries closest to a point selected by a user,identifying a second pixel on the other of said detected boundariesclosest to said point selected by said user and extending marking ofsaid detected boundaries beyond the marked region of interest bydetermining vessel boundary pixels in said image adjacent to the firstand second pixels.
 16. A method according to claim 12, including theactivities of automatically identifying vessel boundaries having aluminance variation below a predetermined threshold and enhances saidluminance variation by use of an enhancement factor and detecting avessel boundary by detecting an edge representing a change in theenhanced luminance variation.
 17. An anatomical blood vessel detectionand display system, comprising: a user interface enabling a user, via adisplay of an image presenting anatomical vessels, to, mark a region ofinterest of a vessel identified by detected boundaries in said image andextend the marked region of interest of said vessel and prevents a userfrom marking said region of interest outside a vessel boundary byextending the marking of said detected boundaries in the direction of apoint selected by a user on said vessel via said user interface; adetector for automatically detecting a boundary of said vessel in theextended marked region of interest by detecting an edge representing achange in luminance in data representing said image; and an image dataprocessor for identifying a minimum vessel diameter in the extendedmarked region of interest between the detected boundaries.
 18. A systemaccording to claim 17, wherein said image data processor identifies thelocation of the minimum vessel diameter in said image, the marked regionof interest of said vessel is extended by, identifying first pixels onadjacent detected vessel boundaries and extending marking of thedetected adjacent vessel boundaries by identifying pixels adjacent tosaid first pixels on the boundaries in the direction of said pointselected by said user.
 19. A system according to claim 18, wherein saiddetector detects individual pixels comprising said edge representingsaid change in luminance in response to determining a luminance gradientexceeding a predetermined threshold and by fitting a curve to individualsegmented groups of substantially adjacent pixels.
 20. A systemaccording to claim 17, wherein the marked region of interest of saidvessel is identified by visual attribute comprising at least one of, (a)highlighting, (b) different color, (c) different shading, (d) differentpattern and (e) dashed line, said detector automatically identifiesvessel boundaries having a luminance variation below a predeterminedthreshold and enhances said luminance variation by use of an enhancementfactor and detects a vessel boundary by detecting an edge representing achange in the enhanced luminance variation.
 21. A system according toclaim 17, wherein said detector detects said edge representing saidchange in luminance in response to determining a luminance gradientexceeding a predetermined threshold.
 22. A system according to claim 17,wherein said user interface enables a user to extend the marked regionof interest of said vessel into a bifurcation of said vessel.